Abstract: Effective multi-scale feature representation is crucial for accurately segmenting lesions of varying sizes in medical images. Addressing the challenge of existing methods failing to fully exploit multi-scale information for different targets, a novel multi-scale perception medical image segmentation method was proposed based on semantic diffusion alignment. It first explored the perceptual ability of multi-scale contextual information from both local and global perspectives to construct multi-scale encoder and decoder. The multi-scale encoder utilized a local multi-scale self-attention mechanism and global fine-tuning to extract features at multiple scales, capturing information from different targets in the image. The multi-scale decoder restored spatial resolution through upsampling while preserving detailed information to achieve more precise segmentation results. Furthermore, in order to further enhance the semantic representation of features, a semantic diffusion alignment module was proposed, which realizes the semantic alignment of low-level features and high-level features, thus obtaining more discriminative fusion features. Finally, experimental results on the Synapse dataset and ACDC dataset demonstrate that the proposed method achieves average Dice similarity coefficients of 82.42% and 92.25% respectively, outperforming most existing methods. Additionally, the relevant code can be found at https://github.com/MiniCoCo-be/MSPSN.