Interactive　image　segmentation　can　improve　segmentation　performance　using　manual　intervention.　Traditional　interactive　segmentation　methods　have　unsatisfactory　segmentation　accuracy　for　images　with　complex　background.　Deep　learning-based　methods　depend　on　large　and　accurate　annotated　datasets.　In　this　paper,　we　propose　an　online　interactive　segmentation　method　based　on　graph　convolutional　network　(GCN),　which　includes　the　superiorities　of　these　two　types　of　methods.　We　present　a　pre-segmentation　stage　to　get　an　initial　segmentation　of　the　image,　then　propose　an　interactive　GCN　(iGCN)　module　to　further　improve　the　accuracy　of　the　initial　segmentation.　Moreover,　iGCN　module　is　trained　online　without　any　pre-training　burden.　Experimental　results　show　that　our　method　outperforms　several　state-of-the-art　methods　on　GrabCut　and　Berkeley　datasets.　©　2021　SPIE.