Official monitoring of deforestation in the Brazilian Amazon has relied traditionally on human experts who visually evaluate remote sensing images and label each individual pixel as deforestation or no deforestation. That methodology is obviously costly and time-consuming due to the vast monitored area. The reason for not using fully automatic methods for the task is the need for the highest possible accuracies in the authoritative deforestation figures. In this work, a semi-automatic, deep learning-based alternative is proposed, in which a deep neural network is first trained with existing images and references from previous years, and employed to perform deforestation detection on recent images. After inference, the uncertainty in the network s pixel-level results is estimated, and it is assumed that low-uncertainty classification results can be trusted. The remaining high-uncertainty regions, which correspond to a small percentage of the test area, are then submitted to post classification, e.g., an auditing procedure carried out visually by a human specialist. In this way, the manual labeling effort is greatly reduced. We investigate various uncertainty estimation strategies, including confidence-based approaches, Monte Carlo Dropout (MCD), deep ensembles and evidential learning, and evaluate different uncertainty metrics. Furthermore, we conduct a comprehensive analysis to identify the characteristics of forest areas that contribute to high uncertainty. We illustrate the main conclusions of the analysis upon 25 selected polygons on four target sites, which exemplify common causes of uncertainty. The target sites are located in challenging study areas in the Brazilian Amazon and Cerrado biomes. Through experimental evaluation on those sites, we demonstrate that the proposed semi-automated methodology achieves impressive F1-score values which exceeds 97 percent, while reducing the visual auditing workload to just 3 percent of the target area. The current code is available at https://github.com/DiMorten/deforestation_uncertainty.