Efficiently accounting for uncertainties in computational fluid dynamics (CFD) models remains a crucial challenge. For computing statistical solutions of partial differential equations in fluid flow models, it is promising to combine scalable deterministic solvers based on lattice Boltzmann methods (LBMs), such as OpenLB, with uncertainty quantification (UQ) techniques. By sampling uncertain parameters and conducting many simulations, the uncertainty can be accurately quantified using statistical analysis. However, non-intrusive Monte Carlo (MC) methods are often computationally expensive when applied to CFD problems. In this talk, we propose to employ a generalized polynomial chaos (gPC) expansion method within a stochastic Galerkin (SG) framework on LBM. Our novel SG LBM offers a more efficient alternative to MC for estimating uncertainty in LBM simulations of incompressible fluid flows. Numerical results of a Taylor--Green vortex flow problem validate that the SG LBM achieves comparable accuracy to the MC LBM, while significantly reducing the computational costs. By combining the strengths of both the LBM and gPC-based SG methods, we thus provide a robust and intrinsically efficient framework for UQ in CFD simulations.