This paper presents a semi-automatic segmentation technique called Bayesian cut that formulates object boundary detection as the most probable explanation (MPE) of a Bayesian network?s joint probability distribution. A two-layer Bayesian network structure is formulated from a planar graph representing a watershed segmentation of an image. The network?s prior probabilities encode the confidence that an edge in the planar graph belongs to an object boundary while the conditional probability tables (CPTs) enforce global contour properties of closure and simplicity (i.e., no self-intersections). Evidence, in the form of one or more connected boundary points, allows the network to compute the MPE with minimal user guidance. The constraints imposed by CPTs also permit a linear-time algorithm to compute the MPE, which in turn allows for interactive segmentation where every mouse movement recomputes the MPE based on the current cursor position and displays the corresponding segmentation.