Semi-supervised medical image segmentation (SSMIS) seeks to match fully supervised performance while sharply reducing annotation cost. Mainstream SSMIS methods rely on \emph{label-space consistency}, yet they overlook the equally critical \emph{representation-space alignment}. Without harmonizing latent features, models struggle to learn representations that are both discriminative and spatially coherent. To this end, we introduce \textbf{Bilateral Alignment in Representation and Label spaces (BARL)}, a unified framework that couples two collaborative branches and enforces alignment in both spaces. For label-space alignment, inspired by co-training and multi-scale decoding, we devise \textbf{Dual-Path Regularization (DPR)} and \textbf{Progressively Cognitive Bias Correction (PCBC)} to impose fine-grained cross-branch consistency while mitigating error accumulation from coarse to fine scales. For representation-space alignment, we conduct region-level and lesion-instance matching between branches, explicitly capturing the fragmented, complex pathological patterns common in medical imagery. Extensive experiments on four public benchmarks and a proprietary CBCT dataset demonstrate that BARL consistently surpasses state-of-the-art SSMIS methods. Ablative studies further validate the contribution of each component. Code will be released soon.

翻译：半监督医学图像分割（SSMIS）旨在匹配全监督性能的同时显著降低标注成本。主流SSMIS方法依赖于\emph{标签空间一致性}，却忽视了同样关键的\emph{表示空间对齐}。若未协调潜在特征，模型难以学习兼具判别性与空间一致性的表示。为此，我们提出\textbf{表示空间与标签空间双边对齐（BARL）}框架，该统一框架耦合两个协作分支，并在两个空间同时实施对齐。针对标签空间对齐，受协同训练与多尺度解码启发，我们设计了\textbf{双路径正则化（DPR）}与\textbf{渐进认知偏差校正（PCBC）}，以施加细粒度跨分支一致性，同时缓解从粗到细尺度的误差累积。针对表示空间对齐，我们在分支间进行区域级与病灶实例级匹配，显式捕捉医学影像中常见的碎片化、复杂病理模式。在四个公开基准及一个专有CBCT数据集上的大量实验表明，BARL持续超越当前最先进的SSMIS方法。消融研究进一步验证了各组成部分的贡献。代码即将发布。