Medical　image　segmentation　is　a　critical　and　complex　process　in　medical　image　processing　and　analysis.　With　the　development　of　artificial　intelligence,　the　application　of　deep　learning　in　medical　image　segmentation　is　becoming　increasingly　widespread.　Existing　techniques　are　mostly　based　on　the　U-shaped　convolutional　neural　network　and　its　variants,　such　as　the　U-Net　framework,　which　uses　skip　connections　or　element-wise　addition　to　fuse　features　from　different　levels　in　the　decoder.　However,　these　operations　often　weaken　the　compatibility　between　features　at　different　levels,　leading　to　a　significant　amount　of　redundant　information　and　imprecise　lesion　segmentation.　The　construction　of　the　loss　function　is　a　key　factor　in　neural　network　design,　but　traditional　loss　functions　lack　high　domain　generalization　and　the　interpretability　of　domain-invariant　features　needs　improvement.　To　address　these　issues,　we　propose　a　Bayesian　loss-based　Multi-Scale　Subtraction　Attention　Network　(MSAByNet).　Specifically,　we　propose　an　inter-layer　and　intra-layer　multi-scale　subtraction　attention　module,　and　different　sizes　of　receptive　fields　were　set　for　different　levels　of　modules　to　avoid　loss　of　feature　map　resolution　and　edge　detail　features.　Additionally,　we　design　a　multi-scale　deep　spatial　attention　mechanism　to　learn　spatial　dimension　information　and　enrich　multi-scale　differential　information.　Furthermore,　we　introduce　Bayesian　loss,　re-modeling　the　image　in　spatial　terms,　enabling　our　MSAByNet　to　capture　stable　shapes,　improving　domain　generalization　performance.　We　have　evaluated　our　proposed　network　on　two　publicly　available　datasets:　the　BUSI　dataset　and　the　Kvasir-SEG　dataset.　Experimental　results　demonstrate　that　the　proposed　MSAByNet　outperforms　several　state-of-the-art　segmentation　methods.　The　codes　are　available　at　https://github.com/zlxokok/MSAByNet.　©　2024　Elsevier　Ltd