Accurate 3D medical image segmentation requires both fine spatial detail and long-range volumetric context. Although Mamba provides efficient long-sequence modeling, volumetric formulations remain sensitive to spatial serialization order, repeated multi-orientation processing, and fixed aggregation of orientation-specific representations. We propose BiSegMamba, an efficient bidirectional tri-oriented Mamba framework that follows a compact-to-detail and local-to-global processing strategy. A progressive compacting stem shifts semantic reasoning to compact representations while retaining shallow high-resolution features for reconstruction, and a multi-scale spatial mixer captures local anatomical patterns. For global context modeling, volumetric features are serialized along three anatomical orientations and their reversed directions, the resulting six sequences are processed jointly by a shared Mamba operator, and reconciled using learned channel-wise forward-backward weighting. The restored orientation-specific features are subsequently integrated through input-dependent directional fusion. Experiments on an in-house carotid dataset and ACDC, BraTS2023, and AMOS-CT achieve Dice scores of 96.3%, 91.63%, 92.57%, and 89.03%, respectively. Compared with SegMamba-V2 under standardized $128^3$ profiling, BiSegMamba reduces the parameter count by 65.9% (138.77M$\rightarrow$47.38M) and inference latency by 53.6% (129.08$\rightarrow$59.86 ms), while increasing throughput by 115.7% (7.75$\rightarrow$16.71 samples/s). These results demonstrate a favorable accuracy--efficiency trade-off among volumetric Mamba-based segmentation models.

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