Real-time semantic segmentation is a core perception capability for underwater robots and autonomous underwater vehicles (AUVs), yet it remains challenging because underwater imagery often exhibits low contrast, blurred boundaries, and strong appearance degradation under strict onboard computation budgets. This paper proposes MSNet, a multi-supervised two-pathway network that decouples feature learning into a semantic branch for context modeling and a detail branch for preserving high-resolution spatial information. MSNet introduces three complementary supervisory signals: (i) low-frequency semantic supervision derived from smoothed labels to encourage body semantics, (ii) high-frequency detail supervision derived from edge-enhanced labels to improve boundary localization, and (iii) category representation supervision implemented by a Category Representation Enhancement Module (CREM) to strengthen class discrimination at the deepest stage. To prevent auxiliary supervision from amplifying cross-resolution misalignment during fusion, we embed a Bilateral Flow-based Alignment Module (BFAM) into multi-stage feature fusion. Experiments on the SUIM benchmark show that MSNet achieves 79.83% mIoU and 86.57% F-score at 55 FPS with 6.2 M parameters on an RTX 3060 GPU, outperforming mainstream encoder–decoder and two-pathway algorithms. Compared with SFNet and BiSeNet V3, MSNet improves mIoU by 1.52% and 1.89%, and runs 9 FPS faster than SFNet. Ablation studies verify the effectiveness and complementarity of the proposed supervision and alignment strategies, indicating MSNet offers a practical accuracy–speed trade-off for marine engineering applications.