Unmanned　aerial　vehicles　(UAVs)　are　increasingly　utilized　in　target　tracking　scenarios　due　to　their　compact　size　and　agile　movement.　With　the　rapid　advancement　of　artificial　intelligence,　an　increasing　number　of　deep　learning　algorithms,　particularly　Transformer-based　trackers,　are　being　employed　in　UAV　tracking.　However,　these　algorithms　typically　have　substantial　data　requirements.　This　paper　investigates　the　integration　of　active　learning　and　UAV　tracking　to　mitigate　the　dataset　demands　of　these　models.　We　propose　an　active　learning　framework　tailored　for　UAV　target　tracking　scenarios.　Our　method,　called　the　Uncertainty　and　Diversity-based　Active　Learning　for　UAV　Tracking　(UDALT),　aims　to　develop　a　high-performance　tracking　model　by　selecting　the　most　informative　samples　based　on　video-level　uncertainty　and　diversity.　Specifically,　for　the　uncertainty　of　the　tracked　object,　we　introduce　a　new　entropy-based　evaluation　formula　to　assess　unlabeled　samples　and　identify　more　challenging　ones.　For　diversity,　we　first　represent　object　types　by　leveraging　intermediate　features　from　the　model,　then　apply　the　K-means　clustering　algorithm　to　determine　the　cluster　centers　of　known　object　types.　By　calculating　the　distances　of　unlabeled　samples　from　these　centers,　we　ensure　a　balanced　distribution　of　object　types　when　selecting　new　samples.　This　combination　of　uncertainty　and　diversity　effectively　reduces　labeling　costs　while　maintaining　high　tracking　accuracy.　To　further　enhance　tracking　performance　in　challenging　UAV　scenarios,　we　replace　the　traditional　Intersection　over　Union　(IoU)　computation　with　Foacler-IoU.　This　adjustment　allows　the　trained　model　to　better　align　with　difficult　samples,　thereby　improving　model　robustness.　Finally,　we　evaluate　our　algorithm　on　the　UAV123,　UAVDT,　and　DTB70　datasets.　The　results　demonstrate　that　our　UDALT　method　outperforms　several　existing　active　learning　methods,　validating　the　effectiveness　of　our　proposed　tracking　method.