EMCAD is an efficient multi-scale convolutional attention decoder designed for medical image segmentation. It addresses the challenge of high computational costs in decoding mechanisms by introducing a multi-scale depth-wise convolution block and incorporating channel, spatial, and grouped (large-kernel) gated attention mechanisms. These components enhance feature maps and capture intricate spatial relationships while focusing on salient regions. EMCAD achieves state-of-the-art performance with significant reductions in parameters and FLOPs compared to existing methods, achieving 79.4% and 80.3% reductions respectively on 12 medical image segmentation datasets. It is adaptable to different encoders and versatile across segmentation tasks, making it a promising tool for efficient and accurate medical image analysis. The implementation is available at https://github.com/SLDGroup/EMCAD. The decoder uses efficient multi-scale convolutional attention modules (MSCAMs), large-kernel grouped attention gates (LGAGs), and efficient up-convolution blocks (EUCBs) to refine feature maps and produce segmentation outputs. The method is evaluated on various medical image segmentation tasks, demonstrating superior performance with lower computational costs. The results show that EMCAD outperforms existing methods in binary and multi-class segmentation tasks, achieving high DICE scores with fewer parameters and FLOPs. The decoder is also tested on different encoder configurations, showing its adaptability and effectiveness in medical image segmentation.EMCAD is an efficient multi-scale convolutional attention decoder designed for medical image segmentation. It addresses the challenge of high computational costs in decoding mechanisms by introducing a multi-scale depth-wise convolution block and incorporating channel, spatial, and grouped (large-kernel) gated attention mechanisms. These components enhance feature maps and capture intricate spatial relationships while focusing on salient regions. EMCAD achieves state-of-the-art performance with significant reductions in parameters and FLOPs compared to existing methods, achieving 79.4% and 80.3% reductions respectively on 12 medical image segmentation datasets. It is adaptable to different encoders and versatile across segmentation tasks, making it a promising tool for efficient and accurate medical image analysis. The implementation is available at https://github.com/SLDGroup/EMCAD. The decoder uses efficient multi-scale convolutional attention modules (MSCAMs), large-kernel grouped attention gates (LGAGs), and efficient up-convolution blocks (EUCBs) to refine feature maps and produce segmentation outputs. The method is evaluated on various medical image segmentation tasks, demonstrating superior performance with lower computational costs. The results show that EMCAD outperforms existing methods in binary and multi-class segmentation tasks, achieving high DICE scores with fewer parameters and FLOPs. The decoder is also tested on different encoder configurations, showing its adaptability and effectiveness in medical image segmentation.