EMCAD (Efficient Multi-scale Convolutional Attention Decoding) is a novel and efficient multi-scale convolutional attention decoder designed for medical image segmentation. It aims to optimize both performance and computational efficiency by leveraging a unique multi-scale depth-wise convolution block and incorporating channel, spatial, and grouped (large-kernel) gated attention mechanisms. EMCAD enhances feature maps through multi-scale convolutions, capturing intricate spatial relationships and focusing on salient regions. The decoder is highly efficient, requiring only 1.91M parameters and 0.38G FLOPs for a standard encoder. Evaluations on 12 medical image segmentation benchmarks across six tasks show that EMCAD achieves state-of-the-art (SOTA) performance with significant reductions in parameters and FLOPs. The adaptability of EMCAD to different encoders and its versatility across segmentation tasks make it a promising tool for advancing efficient and accurate medical image analysis. The implementation of EMCAD is available at https://github.com/SLDGroup/EMCAD.EMCAD (Efficient Multi-scale Convolutional Attention Decoding) is a novel and efficient multi-scale convolutional attention decoder designed for medical image segmentation. It aims to optimize both performance and computational efficiency by leveraging a unique multi-scale depth-wise convolution block and incorporating channel, spatial, and grouped (large-kernel) gated attention mechanisms. EMCAD enhances feature maps through multi-scale convolutions, capturing intricate spatial relationships and focusing on salient regions. The decoder is highly efficient, requiring only 1.91M parameters and 0.38G FLOPs for a standard encoder. Evaluations on 12 medical image segmentation benchmarks across six tasks show that EMCAD achieves state-of-the-art (SOTA) performance with significant reductions in parameters and FLOPs. The adaptability of EMCAD to different encoders and its versatility across segmentation tasks make it a promising tool for advancing efficient and accurate medical image analysis. The implementation of EMCAD is available at https://github.com/SLDGroup/EMCAD.