With　the　growing　significance　of　data　privacy　protection,　Source-Free　Domain　Adaptation　(SFDA)　has　gained　attention　as　a　research　topic　that　aims　to　transfer　knowledge　from　a　labeled　source　domain　to　an　unlabeled　target　domain　without　accessing　source　data.　However,　the　absence　of　source　data　often　leads　to　model　collapse　or　restricts　the　performance　improvements　of　SFDA　methods,　as　there　is　insufficient　true-labeled　knowledge　for　each　category.　To　tackle　this,　Source-Free　Active　Domain　Adaptation　(SFADA)　has　emerged　as　a　new　task　that　aims　to　improve　SFDA　by　selecting　a　small　set　of　informative　target　samples　labeled　by　experts.　Nevertheless,　existing　SFADA　methods　impose　a　significant　burden　on　human　labelers,　requiring　them　to　continuously　label　a　substantial　number　of　samples　throughout　the　training　period.　In　this　paper,　a　novel　approach　is　proposed　to　alleviate　the　labeling　burden　in　SFADA　by　only　necessitating　the　labeling　of　an　extremely　small　number　of　samples　on　a　one-time　basis.　Moreover,　considering　the　inherent　sparsity　of　these　selected　samples　in　the　target　domain,　a　Self-adaptive　Clustering-based　Active　Learning　(SCAL)　method　is　proposed　that　propagates　the　labels　of　selected　samples　to　other　datapoints　within　the　same　cluster.　To　further　enhance　the　accuracy　of　SCAL,　a　self-adaptive　scale　search　method　is　devised　that　automatically　determines　the　optimal　clustering　scale,　using　the　entropy　of　the　entire　target　dataset　as　a　guiding　criterion.　The　experimental　evaluation　presents　compelling　evidence　of　our　method＇s　supremacy.　Specifically,　it　outstrips　previous　SFDA　methods,　delivering　state-of-the-art　(SOTA)　results　on　standard　benchmarks.　Remarkably,　it　accomplishes　this　with　less　than　0.5%　annotation　cost,　in　stark　contrast　to　the　approximate　5%　required　by　earlier　techniques.　The　approach　thus　not　only　sets　new　performance　benchmarks　but　also　offers　a　markedly　more　practical　and　cost-effective　solution　for　SFADA,　making　it　an　attractive　choice　for　real-world　applications　where　labeling　resources　are　limited.　We　propose　a　novel　approach　to　alleviate　the　labeling　burden　in　SFADA　by　only　necessitating　the　labeling　of　an　extremely　small　number　of　samples　on　a　one-time　basis.　Moreover,　considering　the　inherent　sparsity　of　these　selected　samples　in　the　target　domain,　we　propose　a　Self-adaptive　Clustering-based　Active　Learning　(SCAL)　method　that　propagates　the　labels　of　selected　samples　to　other　datapoints　within　the　same　cluster.　To　further　enhance　the　accuracy　of　SCAL,　we　devise　an　self-adaptive　scale　search　method　that　automatically　determines　the　optimal　clustering　scale,　using　the　entropy　of　the　entire　target　dataset　as　a　guiding　criterion.image