BACKGROUND AND OBJECTIVE: Shape prior models play a vital role for segmentation in medical image analysis. These models are most effective when shape variations can be captured by a parametric distribution, and sufficient training data is available. However, in the absence of these conditions, results are invariably much poorer. In this paper, we propose a novel shape prior model, via dual subspace segment projection learning (DSSPL), to address these challenges. METHODS: DSSPL serves to compose shapes from an ensemble of shape segments where each segment is formed using two subspaces: global shape subspace and segment-specific subspace, each necessary for extracting global shape patterns and local patterns, respectively. This ensures the proposed approach has general shape plausibility in regions of signal drop-out or missing boundary information, and also more localized flexibility. The learned projections are constrained with l2,1 sparse norm terms to extract the most distinguishable features, while the reconstructive properties of DSSPL reduces information loss and leverages the subspaces to provide contiguous shapes without any post-processing. RESULTS: Extensive analysis is performed on three databases from different medical imaging systems across X-Ray, MRI, and ultrasound. DSSPL outperforms all compared benchmarks in terms of shape generalization ability and segmentation performance. CONCLUSIONS: We propose a new shape prior model for segmentation in medical image analysis to address the challenges of modelling complex organ shapes with low sample size training data.