Racial disparities in healthcare expenditures are well-documented, yet the underlying drivers remain complex. This study develops a framework to decompose such disparities through shifts in the distributions of mediating variables, rather than treating race itself as a manipulable exposure. We define disparities as differences in covariate-adjusted outcome distributions across racial groups, and decompose the total disparity into a component attributable to differences in mediator distributions, and a residual component that remains after equalizing those distributions. Using data from the Medical Expenditures Panel Survey (MEPS), we examine the extent to which expenditure disparities would persist or be reduced if mediators such as socioeconomic status (SES), insurance access, health behaviors, or health status were equalized across racial groups. To ensure valid inference, we derive asymptotically linear estimators based on influence-function techniques and flexible machine learning, including super learners and a two-part model designed for the zero-inflated, right-skewed nature of expenditure data. Applying this framework to MEPS data from 2009 and 2016, substantial disparities were observed across all pairwise racial comparisons, with the largest gaps observed between non-Hispanic Whites and Hispanics in both years. Differences in SES and health status were the largest contributors to these disparities, with insurance access also playing a meaningful role, particularly for Hispanic populations, whereas health behaviors contributed minimally. Residual disparities persisted, especially in comparisons involving non-Hispanic Whites, suggesting the influence of unmeasured or structural factors.