Interactive　stereo　image　segmentation　(i.e.,　cutting　out　objects　from　stereo　pairs　with　limited　user　assistance)　is　an　important　research　topic　in　computer　vision.　Given　a　pair　of　images,　users　mark　a　few　foreground/background　pixels,　based　on　which　prior　models　are　formulated　for　labeling　unknown　pixels.　Note　that　color　priors　might　not　help　if　the　marked　foreground　and　background　have　similar　colors.　However,　integrating　multiple　types　of　priors,　e.g.,　color　and　disparity　in　segmenting　stereo　pairs,　is　not　trivial.　This　is　because　differing　pairs　of　images　and　even　differing　pixels　in　the　same　image　might　require　different　proportions　of　the　priors.　Besides,　disparities　of　natural　images　are　too　noisy　to　be　directly　used.　This　paper　presents　a　method　that　can　adaptively　determine　the　proportion　of　the　priors　(color　or　disparity)　for　each　pixel.　Specifically　speaking,　the　segmentation　problem　is　defined　in　the　framework　of　MRF　(Markov　Random　Field).　We　formulate　an　MRF　energy　function　which　is　composed　of　clues　from　the　two　types　of　priors,　as　well　as　neighborhood　smoothness　and　stereo　correspondence　constraints.　The　weights　of　the　color　and　disparity　priors　at　each　pixel　are　treated　as　variables　which　are　optimized　together　with　the　label　(foreground　or　background)　of　the　pixel.　In　order　to　overcome　the　noise　problem,　the　weight　of　the　disparity　prior　is　controlled　by　a　confidence　value　learned　from　data.　The　energy　function　is　optimized　by　using　multi-label　graph　cut.　Experimental　results　show　that　our　method　performs　well.