The following (easy) inequality seems to be quite useful. It also seems to me likely to be known, however I do not know of any reference - is it known?
Given random variables $Y_1,\dots,Y_n$ with $Y_i\in[0,a_i]$ for each $i$, where the $a_i$ are positive real numbers, suppose that there exist a real $p_1$ and functions $p_i(y_1,\dots,y_{i-1})$ for each $2\le i\le n$ with the following properties. For each $i$ we have
$\mathbb{E}(Y_i|Y_1,\dots,Y_{i-1})\le p_i(Y_1,\dots,Y_{i-1})$
and almost surely we have $p_1+\dots+p_n\le\mu$.
Then for each $t>0$ we have
$\Pr(Y_1+\dots+Y_n\ge\mu+t)\le e^{-2t^2/\sum_{i=1}^na_i^2}\,.$
The special case in which each $p_i$ is a constant function is Hoeffding's inequality, but I don't know of a reference for this generalisation.
Despite appearances, it seems unlikely to be a consequence of any martingale inequalities (but I would be interested in knowing if this is false): one can use it to establish concentration as $n\to\infty$ around $n/2$ of the sum $Y_1+\dots+Y_n$ of Bernoulli random variables where $\mathbb{E}(Y_1)=\tfrac12$, the $Y_2,\dots,Y_{n/2}$ have expectation $\tfrac13(1+Y_1)$, and the random variables $Y_{n/2+1},\dots,Y_n$ have expectation $\tfrac13(2-Y_1)$, and the $Y_2,\dots,Y_n$ are independent. The point in this example is that the value of $Y_1+\dots+Y_{n/2}$ is not concentrated. (I know one can analyse this with martingale inequalities by breaking it into two pieces, but this is `cheating' as one doesn't have to do this with the Hoeffding-type inequality).
