A new Bayesian formulation for the image segmentation problem is presented. It is based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators by the minimization of a differentiable function. Comparisons with existing methods on synthetic images are presented, as well as realistic applications to the segmentation of magnetic resonance volumes, to motion segmentation, and to edge-preserving filtering.