Few-shot　learning　can　potentially　learn　the　target　knowledge　in　extremely　few　data　regimes.　Existing　few-shot　medical　image　segmentation　methods　fail　to　consider　the　global　anatomy　correlation　between　the　support　and　query　sets.　They　generally　adopt　a　weak　one-way　information　transmission　that　can　not　fully　explore　the　knowledge　to　segment　query　data.　To　address　this　problem,　we　propose　a　novel　Symmetrical　Supervision　network　based　on　traditional　two-branch　methods.　We　raise　two　main　contributions:　(1)　The　Symmetrical　Supervision　Mechanism　is　leveraged　to　strengthen　the　supervision　of　network　training;　(2)　A　transformer-based　Global　Feature　Alignment　module　is　introduced　to　increase　the　global　consistency　between　the　two　branches.　Experimental　results　on　two　challenging　datasets　(abdominal　segmentation　dataset　CHAOS　and　cardiac　segmentation　dataset　MS-CMRSeg)　show　a　remarkable　performance　compared　to　other　comparing　methods.　©　2022　IEEE.