The segmentation of pelvic organs from CT images is an essential step for prostate radiation therapy. However, due to low tissue contrast and large anatomical variations, it is still challenging to accurately segment these organs from CT images. Among various existing methods, deformable models gain popularity as it is easy to incorporate shape priors to regularize the final segmentation. Despite this advantage, the sensitivity to the initialization is often a pain for deformable models. In this paper, we propose a novel way to guide deformable segmentation, which could greatly alleviate the problem caused by poor initialization. Specifically, random forest is adopted to jointly learn image regressor and classifier for each organ. The image regressor predicts the 3D displacement from any image voxel to the organ boundary based on the local appearance of this voxel. It is used as an external force to drive each vertex of deformable model (3D mesh) towards the target organ boundary. Once the deformable model is close to the boundary, the organ likelihood map, provided by the learned classifier, is used to further refine the segmentation. In the experiments, we applied our method to segmenting prostate, bladder and rectum from planning CT images. Experimental results show that our method can achieve competitive performance over existing methods, even with very rough initialization.