Active learning (AL) and semi-supervised learning (SSL) both aim to reduce annotation costs: AL selectively annotates high-value samples from the unlabeled data, while SSL leverages abundant unlabeled data to improve model performance. Although these two appear intuitively compatible, directly combining them remains challenging due to fundamental differences in their frameworks. Current semi-supervised active learning (SSAL) methods often lack theoretical foundations and often design AL strategies tailored to a specific SSL algorithm rather than genuinely integrating the two fields.In this paper, we incorporate AL objectives into the overall risk formulation within the mainstream pseudo-label-based SSL framework, clarifying key differences between SSAL and traditional AL scenarios. To bridge these gaps, we propose a feature re-alignment module that aligns the features of unlabeled data under different augmentations by leveraging clustering and consistency constraints. Experimental results demonstrate that our module enables flexible combinations of SOTA methods from both AL and SSL, yielding more efficient algorithm performance.