We propose a robust and efficient learning-based deformable model for segmenting regions of interest (ROIs) from structural MR brain images. Different from the conventional deformable-model-based methods that deform a shape model locally around the initialization location, we learn an image-based regressor to guide the deformable model to fit for the target ROI. Specifically, given any voxel in a new image, the image-based regressor can predict the displacement vector from this voxel towards the boundary of target ROI, which can be used to guide the deformable segmentation. By predicting the displacement vector maps for the whole image, our deformable model is able to use multiple non-boundary predictions to jointly determine and iteratively converge the initial shape model to the target ROI boundary, which is more robust to the local prediction error and initialization. In addition, by introducing the prior shape model, our segmentation avoids the isolated segmentations as often occurred in the previous multi-atlas-based methods. In order to learn an image-based regressor for displacement vector prediction, we adopt the following novel strategies in the learning procedure: (1) a joint classification and regression random forest is proposed to learn an image-based regressor together with an ROI classifier in a multi-task manner; (2) high-level context features are extracted from intermediate (estimated) displacement vector and classification maps to enforce the relationship between predicted displacement vectors at neighboring voxels. To validate our method, we compare it with the state-of-the-art multi-atlas-based methods and other learning-based methods on three public brain MR datasets. The results consistently show that our method is better in terms of both segmentation accuracy and computational efficiency.