Deep　learning-based　segmentation　algorithms　for　medical　image　require　massive　training　datasets　with　accurate　annotations,　which　is　costly　since　it　takes　much　human　effort　to　manually　labeling　from　scratch.　Therefore,　interactive　image　segmentation　is　important　and　may　greatly　improve　the　efficiency　and　accuracy　of　medical　image　labeling.　Some　interactive　segmentation　methods　(e.g.　Deep　Extreme　Cut　and　Deepgrow)　may　improve　the　labeling　through　minimal　interactive　input.　However　these　methods　only　utilize　the　initial　manually　input　information,　while　existing　segmentation　results　(such　as　annotations　produced　by　nonprofessionals　or　conventional　segmentation　algorithms)　cannot　be　utilized.　In　this　paper,　an　interactive　segmentation　method　is　proposed　to　make　use　of　both　existing　segmentation　results　and　human　interactive　information　to　optimize　the　segmentation　results　progressively.　In　this　framework,　the　user　only　needs　to　click　on　the　foreground　or　background　of　the　target　individual　on　the　medical　image,　the　algorithm　could　adaptively　learn　the　correlation　between　them,　and　automatically　completes　the　segmentation　of　the　target.　The　main　contributions　of　this　paper　are:　(1)　We　adjusted　and　applied　a　convolutional　neural　network　which　takes　medical　image　data　and　user＇s　clicks　information　as　input　to　achieve　more　accurate　segmentation　of　medical　images.　(2)　We　designed　an　iterative　training　strategy　to　realize　the　applicability　of　the　model　to　deal　with　different　number　of　clicks　data　input.　(3)　We　designed　an　algorithm　based　on　false　positive　and　false　negative　regions　to　simulate　the　user＇s　clicks,　so　as　to　provide　enough　training　data.　By　applying　the　proposed　method,　users　can　easily　extract　the　region　of　interest　or　modify　the　segmentation　results　by　multiple　clicks.　The　experimental　results　of　6　medical　image　segmentation　tasks　show　that　the　proposed　method　achieves　more　accurate　segmentation　results　by　at　most　five　clicks.　©　2021　SPIE.