Vehicle Re-Identification (Re-ID) plays a pivotal role in intelligent transportation, where the Domain Adaptation (DA) technique can deal well with the performance gap between the observable source domain and the unseen target domain. Traditional DA assumes the identities of target domain are unavailable, ignoring the effective identity-related semantic information. However, it is feasible and acceptable to annotate a moderate amount of target data with a certain annotation budget. To address this issue, we propose a novel Two-Stage Active Learning (TSAL) framework to query the identity annotations for the most informative target samples, which could maximize model performance with a limited annotation budget. TSAL contains two important sequential stages: (1) the randomness-enhanced sample-level stage aims to improve the sampling randomness for maximizing data diversity, which is achieved by the sequential combination of the fixed-interval and random sampling strategies. (2) At the identity-focused feature-level stage, a novel Identity-Focus Score (IFS) is utilized to emphasize identity-related features for modeling uncertainty and diversity. Extensive experiments across various vehicle Re-ID datasets indicate that our method can achieve state-of-the-art (SOAT) performance by only annotating 10% target data, significantly outperforming existing baselines.