Automatic image segmentation is a fundamental and challenging work in image analysis. We present a stochastic contour approach that draws the contour by multiple agents stochastically, each driven by a simple policy. A contour confidence map is formed, and the image is partitioned hierarchically according to the probability of being surrounded by an average contour. The segmentation is formed by truncating the hierarchical tree based on the dissimilarity increment. The average contour formed in the stochastic contour approach no longer depends on the initial conditions and tolerates less guaranteed convergence. The stochastic contour evolution provides perturbation to jump out of local minima, while the average contour handles model uncertainty naturally. No training process is involved in this approach. The experimental evaluation on a large amount of images with diverse visual properties has shown robustness and good performance of our technique.