Accurate monitoring of aboveground biomass (AGB) is crucial in preventing grassland degradation and achieving carbon neutrality. Remote sensing data and machine learning-based methods have been widely used to estimate the grassland AGB from national to regional scales due to their unique advantages of low cost and high efficiency. However, in the context of significant spatial heterogeneity, the estimation process for AGB in this category usually has inherent uncertainty. Existing statistical validation methods are unable to characterize the spatial distribution of uncertainty and generally lack consideration of potential uncertainty in grassland AGB. To address this issue, we developed a framework to map the spatial distribution of uncertainty based on the quantile regression forest model. Furthermore, the framework explored the driving factors of uncertainty using the geographical detector model. The research results show that the quantile regression forests model in the framework well-estimated the grassland AGB and characterized its spatial distribution. Also, the spatial pattern of uncertainty was closely related to the AGB and affected the amount of sampling points. Among the multiple factors, the soil-adjusted vegetation indices were the primary driving force of the uncertainty. This research presents an approach for mapping uncertainty in grassland AGB estimation and spatializing estimation error, which could be an effective complement to existing AGB estimation methods and thus facilitate the accurate management of grassland resources.