Accurate　medical　image　segmentation　is　the　foundation　of　clinical　imaging　diagnosis　and　3D　image　reconstruction.　However,　medical　images　often　have　low　contrast　between　target　objects,　greatly　affected　by　organ　movement,　and　suffer　from　limited　annotated　samples.　To　address　these　issues,　we　propose　a　few-shot　medical　image　segmentation　network　with　boundary　category　correction　named　Boundary　Category　Correction　Network　(BCC-Net).　Of　overall　medical　few-shot　learning　framework,　we　first　propose　the　Prior　Mask　Generation　Module　(PRGM)　and　Multi-scale　Feature　Fusion　Module　(MFFM).　PRGM　can　better　localize　the　query　target,　while　MFFM　can　adaptively　fuse　the　support　set　prototype,　the　prior　mask　and　the　query　set　features　at　different　scales　to　solve　the　problem　of　the　spatial　inconsistency　between　the　support　set　and　the　query　set.　To　improve　segmentation　accuracy,　we　construct　an　additional　base-learning　branch,　which,　together　with　the　meta-learning　branch,　forms　the　Boundary　Category　Correction　Framework　(BCCF).　It　corrects　the　boundary　category　of　the　meta-learning　branch　prediction　mask　by　predicting　the　region　of　the　base　categories　in　the　query　set.　Experiments　are　conducted　on　the　mainstream　ABDMR　and　ABD-CT　medical　image　segmentation　public　datasets.　Comparative　analysis　and　ablation　experiments　are　performed　with　a　variety　of　existing　state-of-the-art　few-shot　segmentation　methods.　The　results　demonstrate　that　the　effectiveness　of　the　proposed　method　with　significant　enhance　the　segmentation　performance　on　medical　images.