Despite　the　progress　made　in　Mamba-based　medical　image　segmentation　models,　existing　methods　utilizing　unidirectional　or　multi-directional　feature　scanning　mechanisms　struggle　to　effectively　capture　dependencies　between　neighboring　positions,　limiting　the　discriminant　representation　learning　of　local　features.　These　local　features　are　crucial　for　medical　image　segmentation　as　they　provide　critical　structural　information　about　lesions　and　organs.　To　address　this　limitation,　we　propose　SliceMamba,　a　simple　yet　effective　locally　sensitive　Mamba-based　medical　image　segmentation　model.　SliceMamba　features　an　efficient　Bidirectional　Slicing　and　Scanning　(BSS)　module,　which　performs　bidirectional　feature　slicing　and　employs　varied　scanning　mechanisms　for　sliced　features　with　distinct　shapes.　This　design　keeps　spatially　adjacent　features　close　in　the　scan　sequence,　preserving　the　local　structure　of　the　image　and　enhancing　segmentation　performance.　Additionally,　to　fit　the　varying　sizes　and　shapes　of　lesions　and　organs,　we　introduce　an　Adaptive　Slicing　Search　method　that　automatically　identifies　the　optimal　feature　slicing　method　based　on　the　characteristics　of　the　target　data.　Extensive　experiments　on　two　skin　lesion　datasets　(ISIC2017　and　ISIC2018),　two　polyp　segmentation　datasets　(Kvasir　and　ClinicDB),　one　ultra-wide　field　retinal　hemorrhage　segmentation　dataset　(UWF-RHS),　and　one　multi-organ　segmentation　dataset　(Synapse)　demonstrate　the　effectiveness　of　our　method.　©　2013　IEEE.