For decision-makers considering new medicines for reimbursement and public use, both value for money and affordability are important considerations. Whereas a cost-effectiveness model provides information about value for money, a budget impact assessment (BIA) is customized to a specific context and estimates the total investment needed; one part of affordability. Both analytic approaches have parameter uncertainty within them, yet comparatively little attention is given to parameter uncertainty in BIA. Currently, within BIA, uncertainty exploration is limited to point estimates for plausible scenarios, prompting the question: can a decision-maker be confident in point estimates? Within this paper, our intent is to revitalize the discussion of uncertainty in BIA. In the context of health technology assessments submitted to support reimbursement decision-making, we propose reliance on probabilistic sensitivity analysis conducted in the cost-effectiveness model. If assumptions made in a cost-effectiveness model are valid, probabilistic cost estimates from the model, with the same perspective adopted as the BIA, should also inform BIA. Mean and variance of population outcomes, given parameter uncertainty in model inputs, are estimable from model outputs. As sufficiently large random samples are drawn from a population, the distribution of sample means will follow an approximately normal distribution. Therefore, when drawing samples from the model to inform estimates of budget impact, the assumption of an approximately normal distribution for costs is reasonable. We propose that the variance in mean costs from the cost-effectiveness model also reflects the variance in budget impact estimates and should be used to estimate budget impact confidence intervals.