Medical　images　exhibit　multi-granularity　and　high　obscurity　along　boundaries.　As　representative　work,　the　U-Net　and　its　variants　exhibit　two　shortcomings　on　medical　image　segmentation:　(a)　they　expand　the　range　of　reception　fields　by　applying　addition　or　concatenate　operators　to　features　with　different　reception　fields,　which　disrupts　the　distribution　of　the　essential　feature　of　objects;　(b)　they　utilize　the　downsampling　or　atrous　convolution　to　characterize　multi-granular　features　of　objects,　which　can　obtain　a　large　range　of　reception　fields　but　leads　to　blur　boundaries　of　objects.　A　Shuffling　Atrous　Convolutional　U-Net　(SACNet)　for　circumventing　those　issues　is　proposed.　The　significant　component　of　SACNet　is　the　Shuffling　Atrous　Convolution　(SAC)　module,　which　fuses　different　atrous　convolutional　layers　together　by　using　a　shuffle　concatenate　operation,　so　that　the　features　from　the　same　channel　(which　correspond　to　the　same　attribute　of　objects)　are　merged　together.　Besides　the　SAC　modules,　SACNet　utilizes　an　EP　module　during　the　fine　and　medium　levels　to　enhance　the　boundaries　of　objects,　and　utilizes　a　Transformer　module　during　the　coarse　level　to　capture　an　overall　correlation　of　pixels.　Experiments　on　three　medical　image　segmentation　tasks:　abdominal　organ,　cardiac,　and　skin　lesion　segmentation　demonstrate　that,　SACNet　outperforms　several　state-of-the-art　methods　and　facilitates　easy　transplant　to　other　semantic　segmentation　tasks.