Semi-Supervised Semantic Segmentation (SSS) has emerged as an effective paradigm to reduce the reliance on dense pixel-wise annotations. However, existing pseudo-labeling approaches often suffer from instability and semantic inconsistency, particularly in complex consumer electronic environments where visual inputs are noisy and heterogeneous. To overcome these limitations, we propose a unified framework termed Reliability-Guided Consistency Learning (RGCL), which systematically enhances probabilistic stability and semantic reliability through temporally and spatially consistent supervision. At the core of RGCL lies the Variance-Guided Prior Augmentation (VGPA), which quantifies temporal prediction variance across training checkpoints to identify and weight reliable unlabeled samples, thereby constructing a stability-aware prior. Building upon this, the Dual-Stream Pseudo-Label Refinement (DSPL) module distills high-confidence pseudo-labels from the variance-filtered subset and jointly optimizes them with labeled data in a dual-stream manner, promoting boundary accuracy and structural coherence. To further regularize the predictive manifold, the Interpolation Consistency Regularization (ICR) enforces geometric smoothness by coupling interpolation in the input domain with consistency in the predictive space, ensuring local continuity and mitigating spurious confidence transitions. By jointly addressing temporal uncertainty, semantic reliability, and manifold smoothness, RGCL establishes a reliability-driven learning paradigm for semi-supervised segmentation. Extensive experiments on Cityscapes, Pascal VOC, and ADE20K benchmarks demonstrate that RGCL consistently achieves state-of-the-art performance under various label ratios, confirming its robustness and effectiveness for deployment in consumer electronic applications.
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