The paper introduces UU-Mamba, a novel model for cardiac MRI image segmentation that integrates the U-Mamba architecture with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SAM) optimizer. The UU-Mamba model aims to improve the accuracy and robustness of cardiac MRI segmentation by addressing challenges such as overfitting and data demands. The uncertainty-aware loss function combines region-based, distribution-based, and pixel-based losses to enhance segmentation performance by prioritizing confident predictions and reducing the impact of ambiguous data. The SAM optimizer helps the model find flat minima in the loss landscape, improving generalization and reducing overfitting. The model is evaluated on the ACDC dataset, outperforming state-of-the-art models including TransUNet, Swin-UNet, mUNet, and nnFormer in terms of Dice Similarity Coefficient (DSC) and Mean Squared Error (MSE). The UU-Mamba model demonstrates superior segmentation accuracy and robustness, making it a promising approach for advanced cardiac MRI analysis.The paper introduces UU-Mamba, a novel model for cardiac MRI image segmentation that integrates the U-Mamba architecture with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SAM) optimizer. The UU-Mamba model aims to improve the accuracy and robustness of cardiac MRI segmentation by addressing challenges such as overfitting and data demands. The uncertainty-aware loss function combines region-based, distribution-based, and pixel-based losses to enhance segmentation performance by prioritizing confident predictions and reducing the impact of ambiguous data. The SAM optimizer helps the model find flat minima in the loss landscape, improving generalization and reducing overfitting. The model is evaluated on the ACDC dataset, outperforming state-of-the-art models including TransUNet, Swin-UNet, mUNet, and nnFormer in terms of Dice Similarity Coefficient (DSC) and Mean Squared Error (MSE). The UU-Mamba model demonstrates superior segmentation accuracy and robustness, making it a promising approach for advanced cardiac MRI analysis.