Global shape information is an effective top-down complement to bottom-up figure-ground segmentation as well as a useful constraint to avoid drift during adaptive tracking. We propose a novel method to embed global shape information into local graph links in a Conditional Random Field (CRF) framework. Given object shapes from several key frames, we automatically collect a shape dataset onthe- fly and perform statistical analysis to build a collection of deformable shape templates representing global object shape. In new frames, simulated annealing and local voting align the deformable template with the image to yield a global shape probability map. The global shape probability is combined with a region-based probability of object boundary map and the pixel-level intensity gradient to determine each link cost in the graph. The CRF energy is minimized by min-cut, followed by Random Walk on the uncertain boundary region to get a soft segmentation result. Experiments on both medical and natural images with deformable object shapes are demonstrated.