Data scarcity is a primary bottleneck in 3D medical image segmentation: expert voxel-level annotations are expensive, and privacy regulations often severely restrict access to unannotated clinical volumes. While semi-supervised learning (SSL) aims to alleviate annotation burdens, existing paradigms implicitly rely on large pools of unlabeled data ($U$) relative to labeled data ($L$), assuming $U \gg L$ to filter noise and enforce consistency. When restricted to the data-constrained regime where unlabeled data are limited ($U \le L$), standard SSL methods suffer from severe pseudo-label degeneracy, performance degradation, and high seed-to-seed variance. In this work, we propose BoundarySeg, a unified multi-task semi-supervised framework specifically designed for the limited unlabeled data regime ($U \le L$). BoundarySeg formulates volumetric segmentation as a joint learning task that predicts whole-organ geometry alongside auxiliary organ boundaries. Rather than relying on large cohorts to average out pseudo-label noise, BoundarySeg employs the auxiliary boundary branch as an internal geometric anchor. Coupled with uncertainty-weighted mean-teacher consistency across both tasks alongside axis-flip equivariance and copy-paste regularization, this joint supervision stabilizes spatial representations and prevents segmentation collapse when $U \le L$. Across multiple 3D segmentation datasets, BoundarySeg maintains high performance stability as unlabeled pool size shrinks, consistently outperforming state-of-the-art semi-supervised frameworks in low-data settings. \href{https://github.com/tushaarkataria/BoundarySeg}{https://github.com/tushaarkataria/BoundarySeg}

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