With the needs of efficient acquisition of soil information, Digital Soil Mapping (DSM) has been greatly developed and widely applied for over the past two decades. The spatial estimates of soil properties produced with diverse methods over various study areas, have been often seen as the main output of DSM, as they play an important role in environmental modelling and policy. However, compared with the soil property maps, their prediction uncertainty is still less emphasized, which may potentially lead to mis-uses of results and inappropriate decisions if the uncertainty is not assessed, reported, and taken into account by end-users.In this communication, we present a preliminary review of the sources of prediction uncertainties in DSM coming from learning soil data (data source, sampling in space and time, measurements), covariates, and models. We also summarize the methods used to estimate the uncertainty, and to assess the reliability of the uncertainty estimates. We also consider the propagation of uncertainties when several soil attributes are combined to derive information and/or used as inputs for modelling. Furthermore, we discuss some strategies for mitigating the uncertainty, challenges, and future perspectives. This review aims to consolidate the understanding of DSM uncertainties and to contribute to reliable DSM practices, facilitating more informed decision-making in soil-related research and management.