Sandy beaches comprise large parts of the world's shorelines and act as a natural buffer for many exposed people and assets that are concentrated in the coastal zone. Many coastal communities are vulnerable to the impact of sea-level rise (SLR) that can amplify the episodic erosion from storms and drive structural erosion. The way communities adapt to SLR hinge critically on future SLR projections. One of the major uncertainties is the potential rapid disintegration of large fractions of the Antarctic ice sheet (AIS) that can accelerate sea-level rise, albeit neglected in the latest SLR estimates of the 'Intergovernmental Panel on Climate Change (IPCC)'. Accounting for rapid AIS mass loss in coastal impact assessments is essential for risk-averse coastal managers that disfavour events with large consequences. Although methods to predict future erosion estimates under SLR have been developed, hitherto no study has assessed the impact of different cases of AIS dynamics to erosion estimates. Here, a case-study to the island of Sint Maarten is considered to evaluate the implications for strategies to manage coastal erosion under SLR uncertainty. Regional SLR projections are made for a case consistent with the IPCC, a case with a skewed probability distribution function of the AIS dynamics and a high-end scenario of Antarctic mass loss. SLR projections are incorporated within a probabilistic erosion framework using synthetic storm time series for two beaches on the island. Future retreat distances from storms and long term coastal recession are calculated, and the different scenarios are compared and contrasted. For a future 1/100 year retreat distance of storm erosion, often used for zoning policies, estimates may be exceeded up to 1.11-2.22 times as frequent for inclusion of skewness, and 2.22-67 times as frequent for the high-end scenario compared to the IPCC case. These numbers further increase when additional climate model uncertainty is introduced. In terms of long-term recession, the 1% exceedance probability in 2100 for the IPCC case has a 2-4.5% exceedance probability for a skewed distribution function and a 37-88% exceedance probability under a high-end scenario of the AIS. Lower exceedance probabilities, essential for risk-averse coastal managers, are underestimated relatively more leading to potential disillusion about the safety level that is set. In conclusion, precluding AIS uncertainty from SLR projections that feed coastal impact assessments may lead to ill-informed decisions on SLR adaptation. Risk-averse coastal managers should thus be better informed on deep uncertainty in SLR projections to prevent maladaptation of vulnerable areas.