Passive acoustic monitoring has grown in utility for tracking wildlife populations, although challenges remain when using acoustic detections to monitor population size and density. Distance sampling is considered the ‘gold standard’ for estimating animal densities but has several important limitations, especially for rare, cryptic, and high-density species. Here, we test the performance of a simple, quickly derived bioacoustic indicator for monitoring population density: call density—the proportion of recording samples containing vocalizations. Over three years, we collected synchronized bioacoustic and point-transect distance sampling data for eight forest bird species native to the Island of Hawai‘i, including four endangered species, across diverse ecosystems ranging from subalpine dry woodland to montane rainforest. The species studied exhibit varied population structures, from gregarious flocks to small, territorial family groups. Our results revealed significant, strong correlations between call density and distance sampling-based animal density estimates for all species, demonstrating that call density is a reliable indicator of animal density that can be used independently or in combination with traditional monitoring methods. Our analysis uses a fixed amount of manual validation of machine learning classifier output examples, without requiring prohibitively high classifier performance, and is robust to variation in vocal activity rates across time and space, making it both adaptable and scalable. This approach could enhance passive acoustic monitoring by providing a more sensitive population health indicator than commonly used detection/nondetection methods, facilitating prompt conservation and management decisions, particularly for species that are difficult to monitor with distance sampling.