Radio-frequency–based drone classification is a critical capability for modern antidrone systems. However, the development of dependable artificial intelligence models in this domain is hindered by the high cost and complexity of expert data labeling. This challenge is particularly pronounced in radio-frequency signal analysis, where labeled datasets are typically small, partially unlabeled, and continuously evolving during system deployment. This paper proposes a tailored active learning framework for drone classification in the radio-frequency domain, integrating human expertise into an iterative learning process that selectively queries the most informative unlabeled samples. By prioritizing sample informativeness, the proposed framework aims to achieve high detection performance while significantly reducing labeling effort. The approach is evaluated using the VTI_USRP_DroneSET dataset, comprising radio-frequency spectrograms acquired in realistic outdoor conditions within the 2.4 GHz frequency band. Experimental results demonstrate that the proposed active learning strategy achieves mAP50–95 performance comparable to conventional supervised learning while requiring only one quarter of the labeled data. The results confirm that active learning enables data-efficient radio-frequency based drone classification without compromising detection accuracy. Furthermore, near-optimal performance is consistently obtained with an optimal training duration of 80 epochs, reducing both annotation and computational costs. These findings confirm that active learning provides a data-efficient and cost-effective solution for RF-based drone classification and is well suited for real-time deployment in operational antidrone systems where labeled data are scarce and continuously acquired.