Medical image segmentation is one of the core tasks in medical image analysis and plays a vital role in clinical diagnosis, surgical planning, and treatment evaluation. To address the limitations of existing methods in global context modeling, local detail preservation and computational efficiency, this paper proposes a novel medical image segmentation method based on a multi-scale visual state space U-Net. This method integrates a state space modeling mechanism, a multi-scale parallel wavelet convolution module and an improved large-kernel grouped attention gating module, aiming to enhance cross-layer feature fusion capability and detailed feature representation. Furthermore, this paper introduces a hierarchical cascaded prediction strategy, which progressively generates and fuses prediction maps at multiple semantic levels of the decoder, thereby further improving the model′s robustness and adaptability to multi-scale targets. Experimental results on two public datasets, Synapse and automated cardiac diagnosis challenge (ACDC）, demonstrate that the overall performance of the proposed method surpasses existing mainstream methods, validating its effectiveness and superiority.